Disease Prediction
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Greatest papers with code
Deep EHR: Chronic Disease Prediction Using Medical Notes
Machine Learning for Health Care conference 2018
• NYUMedML/DeepEHR
• 
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.
Deep EHR: Chronic Disease Prediction Using Medical Notes
Machine Learning for Health Care conference 2018
• NYUMedML/DeepEHR
•
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation.
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
Spectral Graph Convolutions for Population-based Disease Prediction
We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting.
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
DISEASE PREDICTION EPIDEMIOLOGY MULTIVARIATE TIME SERIES FORECASTING PROBABILISTIC PROGRAMMING
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records
• astorfi/cor-gan
•
To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
SOTA for Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)
DISEASE PREDICTION IMAGE CLASSIFICATION SYNTHETIC DATA GENERATION
Robust Multiple Kernel k-means Clustering using Min-Max Optimization
To address this problem and inspired by recent works in adversarial learning, we propose a multiple kernel clustering method with the min-max framework that aims to be robust to such adversarial perturbation.
Representation Learning for Medical Data
We propose a representation learning framework for medical diagnosis domain.
DISEASE PREDICTION MEDICAL DIAGNOSIS REPRESENTATION LEARNING